Artificial neural network for redundant manipulator inverse kinematics computation

Sui-Hoi Hou, Wibawa Utama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

A redundant manipulator can be defined as a manipulator that has more degrees of freedom than necessary to determine the position and orientation of the end-effector. Such a manipulator has dexterity, flexibility, and the ability to maneuver in the presence of obstacles. This paper presents a solution to the inverse kinematics problem for redundant manipulator based on artificial neural network (ANN). The ANN used is of the supervised type - multilayer feedforward neural network with back error propagation (BEP) training algorithm. The training set for the ANN is obtained by sampling the joint space trajectory of the redundant manipulator arm or from the joint angle encoders of the manipulator. These sampled values of the task space trajectory (end-effector coordinates) are used as the command input vectors to the ANN. By presenting the network with these training set cyclically during training time, the BEP algorithm will change the learning parameters of the ANN so that the sum of the squared difference between the actual joint coordinates and the desired output vectors is minimized.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsDavid P. Casasent
PublisherPubl by Int Soc for Optical Engineering
Pages668-677
Number of pages10
ISBN (Print)0819407445
StatePublished - Jan 1 1992
EventIntelligent Robots and Computer Vision X: Algorithms and Techniques - Boston, MA, USA
Duration: Nov 11 1991Nov 13 1991

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1607
ISSN (Print)0277-786X

Other

OtherIntelligent Robots and Computer Vision X: Algorithms and Techniques
CityBoston, MA, USA
Period11/11/9111/13/91

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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